Causal Interaction in Factorial Experiments: Application to Conjoint Analysis
析因实验中的因果交互作用：联合分析应用AI 解读 ↓
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析因实验中的因果交互作用：联合分析应用
- 研究问题
- 在多个因素及水平随机组合的析因实验中，如何定义便于解释且不依赖基准条件选择的因果交互效应，并进行估计？
- 方法与思路
- 作者提出平均边际交互效应 AMIE，以带加权零和约束的 ANOVA 回归估计，并通过惩罚化框架合并水平和选择因素。
- 材料陈述的贡献
- 摘要称，AMIE 的相对大小不依赖基准条件，正则化程序减少错误发现，并在非洲族群投票联合分析中发现交互模式。
- 阅读关注点
- 应核对 AMIE 的平均方式与随机化设计、基准不变性的确切含义，以及正则化后错误发现减少的证据和推断方式。
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原文摘要
We study causal interaction in factorial experiments, in which several factors, each with multiple levels, are randomized to form a large number of possible treatment combinations. Examples of such experiments include conjoint analysis, which is often used by social scientists to analyze multidimensional preferences in a population. To characterize the structure of causal interaction in factorial experiments, we propose a new causal interaction effect, called the average marginal interaction effect (AMIE). Unlike the conventional interaction effect, the relative magnitude of the AMIE does not depend on the choice of baseline conditions, making its interpretation intuitive even for higher-order interactions. We show that the AMIE can be nonparametrically estimated using ANOVA regression with weighted zero-sum constraints. Because the AMIEs are invariant to the choice of baseline conditions, we directly regularize them by collapsing levels and selecting factors within a penalized ANOVA framework. This regularized estimation procedure reduces false discovery rate and further facilitates interpretation. Finally, we apply the proposed methodology to the conjoint analysis of ethnic voting behavior in Africa and find clear patterns of causal interaction between politicians’ ethnicity and their prior records. The proposed methodology is implemented in an open source software package. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.